Data-Driven Internal Model Control of an Anaerobic Digestion Process

2021 ◽  
Author(s):  
Larisa Condrachi ◽  
Ramon Vilanova ◽  
Marian Barbu
Keyword(s):  
Anaerobic Digestion ◽  
Internal Model ◽  
Internal Model Control ◽  
Data Driven ◽  
Anaerobic Digestion Process ◽  
Digestion Process ◽  
Model Control

scholarly journals Anaerobic Digestion Process Control Using a Data-Driven Internal Model Control Method

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6746
Author(s):  
Larisa Condrachi ◽  
Ramon Vilanova ◽  
Montse Meneses ◽  
Marian Barbu
Keyword(s):  
Anaerobic Digestion ◽  
Internal Model ◽  
Control Method ◽  
Reference Model ◽  
Internal Model Control ◽  
Operating Conditions ◽  
Data Driven ◽  
Anaerobic Digestion Process ◽  
Digestion Process ◽  
Model Control

Anaerobic digestion processes offer the possibility for wastewater treatment while obtaining a benefit through the obtained biogas. This paper aims to continue the effort to adopt data-driven control methods in the case of anaerobic digestion processes. The paper proposes a data-based Internal Model Control approach applied to an anaerobic digestion process. The paper deals extensively with the issue of choosing the reference model and proposing an engineering approach to this issue. The paper also addresses the issue of verifying robust stability, a very important aspect considering the uncertainties that characterize bioprocesses in general. The approach proposed in the paper is validated through a numerical simulation using the Anaerobic Digestion Model No. 1. During the validation of the proposed control solution, the main operating conditions were analyzed, such as the setpoint tracking performance, the rejection of disturbance generated by variations in the influent concentration, and the effect of the measurement noise on the controlled variable.

scholarly journals Data-driven Control of the Activated Sludge Process: IMC plus Feedforward Approach

2016 ◽  
Vol 11 (4) ◽  
pp. 522 ◽  
Author(s):  
Jose David Rojas ◽  
Orlando Arrieta ◽  
Montse Meneses ◽  
Ramon Vilanova
Keyword(s):  
Activated Sludge ◽  
Substrate Concentration ◽  
Internal Model ◽  
Internal Model Control ◽  
Data Driven ◽  
Activated Sludge Process ◽  
Virtual Reference ◽  
Model Free ◽  
Model Control ◽  
Feedforward Controller

<p>In the work presented in this paper, data-driven control is used to tune an Internal Model Control. Despite the fact that it may be contradictory to apply a model-free method to a model-based controller, this methodology has been successfully applied to a Activated Sludge Process (ASP) based wastewater treatment. In addition a feedforward controller over the influent substrate concentration was also computed using the virtual reference feedback tuning and applied to the same wastewater process to see the effect over the dissolved oxygen and the substrate concentration at the effluent.</p>

scholarly journals Analysis of the ROA of an anaerobic digestion process via data-driven Koopman operator

2021 ◽  
Vol 10 (1) ◽  
pp. 109-131
Author(s):  
Camilo Garcia-Tenorio ◽  
Eduardo Mojica-Nava ◽  
Mihaela Sbarciog ◽  
Alain Vande Wouwer
Keyword(s):  
Anaerobic Digestion ◽  
Initial Conditions ◽  
Data Driven ◽  
Koopman Operator ◽  
Anaerobic Digestion Process ◽  
Digestion Process ◽  
Biochemical Systems ◽  
Data Driven Approach ◽  
Selection Of

Abstract Nonlinear biochemical systems such as the anaerobic digestion process experience the problem of the multi-stability phenomena, and thus, the dynamic spectrum of the system has several undesired equilibrium states. As a result, the selection of initial conditions and operating parameters to avoid such states is of importance. In this work, we present a data-driven approach, which relies on the generation of several system trajectories of the anaerobic digestion system and the construction of a data-driven Koopman operator to give a concise criterion for the classification of arbitrary initial conditions in the state space. Unlike other approximation methods, the criterion does not rely on difficult geometrical analysis of the identified boundaries to produce the classification.

scholarly journals Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments—The Wastewater Treatment Plant Control Case

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3743
Author(s):  
Ivan Pisa ◽  
Antoni Morell ◽  
Jose Lopez Vicario ◽  
Ramon Vilanova
Keyword(s):  
Neural Networks ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Internal Model ◽  
Treatment Plant ◽  
Internal Model Control ◽  
Data Driven ◽  
Control Performance ◽  
Model Control ◽  
Artificial Neural

The evolution of industry towards the Industry 4.0 paradigm has become a reality where different data-driven methods are adopted to support industrial processes. One of them corresponds to Artificial Neural Networks (ANNs), which are able to model highly complex and non-linear processes. This motivates their adoption as part of new data-driven based control strategies. The ANN-based Internal Model Controller (ANN-based IMC) is an example which takes advantage of the ANNs characteristics by modelling the direct and inverse relationships of the process under control with them. This approach has been implemented in Wastewater Treatment Plants (WWTP), where results show a significant improvement on control performance metrics with respect to (w.r.t.) the WWTP default control strategy. However, this structure is very sensible to non-desired effects in the measurements—when a real scenario showing noise-corrupted data is considered, the control performance drops. To solve this, a new ANN-based IMC approach is designed with a two-fold objective, improve the control performance and denoise the noise-corrupted measurements to reduce the performance degradation. Results show that the proposed structure improves the control metrics, (the Integrated Absolute Error (IAE) and the Integrated Squared Error (ISE)), around a 21.25% and a 54.64%, respectively.

Data-driven techniques for fault detection in anaerobic digestion process

2021 ◽  
Vol 146 ◽  
pp. 905-915
Author(s):  
Pezhman Kazemi ◽  
Christophe Bengoa ◽  
Jean-Philippe Steyer ◽  
Jaume Giralt
Keyword(s):  
Anaerobic Digestion ◽  
Fault Detection ◽  
Data Driven ◽  
Anaerobic Digestion Process ◽  
Digestion Process

FRIT of Internal Model Controllers for Poorly Damped Linear Time Invariant Systems: Kautz Expansion Approach

2016 ◽  
Vol 28 (5) ◽  
pp. 745-751 ◽  
Author(s):  
Hnin Si ◽  
◽  
Osamu Kaneko ◽  
Keyword(s):  
Internal Model ◽  
Linear Time ◽  
Expansion Method ◽  
Parameter Tuning ◽  
Internal Model Control ◽  
Data Driven ◽  
Linear Time Invariant ◽  
Model Control ◽  
Linear Time Invariant Systems ◽  
Time Invariant

[abstFig src='/00280005/18.jpg' width='300' text='Data-driven approach to internal model controller with tunable parameters' ] This paper addresses the tuning of data-driven controllers for poorly damped linear time-invariant systems in the internal model control (IMC) architecture. In this study, fictitious reference iterative tuning (FRIT), which is one of the controller parameter tuning methods with the data obtained from a one-shot experiment, is used for tuning the controller. The Kautz expansion method in which the coefficients are tunable parameters is introduced to approximate the dynamics of linear time-invariant systems, which have poor damping characteristics. Such an approximated model with tunable parameters is implemented in the IMC architecture. A model and a controller can be realized simultaneously with a one-shot experiment by tuning the IMC with the parameterized Kautz expansion model and by using FRIT. The validity of the proposed method is examined with a numerical example.

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